Notes on MOSES data reduction 2006-May-24 CCK First run of moses_align_orders was yesterday (log060523.163229). The routine was intentionally limited to registration of just the m=-1 order, for just the first two exposures. Summary of results: CHISQ_BADNESS: chisq = 1744892.8 chisq_reduced = 0.85008617 imx = 24.1310 25.6349 2076.54 2075.01 imy = -3.62048 1019.20 -2.69529 1022.31 % COREGISTRATE: Alignment completed at Tue May 23 16:49:56 2006 ncalls = 555 .... CHISQ_BADNESS: chisq = 4656747.4 chisq_reduced = 2.2666494 imx = 26.4620 24.8723 2075.15 2075.08 imy = -3.50844 1018.37 -1.30873 1024.03 % COREGISTRATE: Alignment completed at Tue May 23 17:09:52 2006 ncalls = 701 moses_align_orders took 39.142551 minutes to run Comments: - Amazingly, my complicated alignment data structure seems to work. - Note that the above are not quite the final numbers, just the last ones reported by calls to CHISQ_BADNESS. Inspection of alignment[i].minus.cpoints.x[j] confirms that these are close to the final values, though. For the record, here are the final positions from the save file: IDL> print, alignment[0].minus.cpoints.x 24.1406 25.6308 2076.53 2074.99 IDL> print, alignment[0].minus.cpoints.y -3.60734 1019.23 -2.75141 1022.29 IDL> print, alignment[1].minus.cpoints.x 26.4619 24.8725 2075.15 2075.08 IDL> print, alignment[1].minus.cpoints.y -3.50831 1018.37 -1.30909 1024.03 - I would expect the alignment to be the same for each exposure, and the hoped-for consistency is 0.1 pixel. This is what I in fact get: IDL> print, alignment[0].minus.cpoints.x - alignment[1].minus.cpoints.x -2.32132 0.758270 1.38257 -0.0922852 IDL> print, alignment[0].minus.cpoints.y - alignment[1].minus.cpoints.y -0.0990317 0.858826 -1.44232 -1.74170 IDL> print, sqrt( (alignment[0].minus.cpoints.x - alignment[1].minus.cpoints.x)^2 + (alignment[0].minus.cpoints.y - alignment[1].minus.cpoints.y)^2 ) 2.32343 1.14567 1.99794 1.74414 - The alignment values for the two exposures are only broadly consistent, with errors of 1.1-2.3 pixels. Admittedly, this is from analyzing the shortest exposures, so perhaps things will improve with deeper images. - Lots of function calls reported by amoeba. I should probably reuse imx and imy with subsequent calls to speed things up. - Value of reduced chi squared differs between these two alignment cases. I wonder why. - Looks like the complete run would take 0.5 * 39min * 2 orders * 27 exposures = 17.6 hrs. - I've examined residuals from the save file, and conclude that the alignment is nowhere near done. I'm not sure what is wrong, but clearly this can be improved. Here is how I made the residuals, and then saved them as jpegs to look at later: IDL> norm = median(cube_minus[*,*,1])/median(cube_plus[*,*,1]) IDL> print,norm 2.21052 IDL> foo1=cube_minus[*,*,1]-cube_plus[*,*,1]*norm IDL> foo2=cube_minus[*,*,0]-cube_plus[*,*,0]*norm IDL> write_jpeg, '20060524_residuals_exp0.jpg',bytscl(foo1) % Loaded DLM: JPEG. IDL> write_jpeg, '20060524_residuals_exp1.jpg',bytscl(foo2) IDL> print, median(abs(foo1)) 55.2104 IDL> print, median(abs(foo2)) 14.5912 - It looks as though the residuals for the second exposure are smaller than the first, even though the second exposure was longer. This is consistent with the differing values of chi squared. Is it merely that the amoeba didn't converge?